code: def resolve_sentences(self, sentences):
      batch_is_word_start = []
      batch_token_ids = []
      batch_tokens = []
  
      for sentence in sentences:
          tokens, is_word_start = self.tokenize_sentence(sentence)
          token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
          to_input = np.pad(
              token_ids,
              [(0, self.max_length - len(token_ids))],
              mode="constant",
          )
          batch_token_ids.append(to_input)
          batch_tokens.append(tokens)
          batch_is_word_start.append(is_word_start)
  
      embeddings = self.session.run(
          self.embeddings_op,
          feed_dict={self.token_ids_op: batch_token_ids},
      )
  
      result = []
      for i in range(len(sentences)):
          tokens = batch_tokens[i]
          is_word_start = batch_is_word_start[i]
          item_embeddings = embeddings[i, : len(tokens), :]
  
          resolved = TokenEmbeddings.create_sentence(
              tokens, is_word_start, item_embeddings
          )
          result.append(resolved)
  
      return result
  
imports: import shutil
  import numpy
  import plyvel
  import os.path
  import sys
  from bert.modeling import *
  from bert.tokenization import *
  import json
  import os.path
  import numpy
label_desc: Use image node_id 3 for calling the BertEmbeddingsResolver obj's underlying member method code with example usage: obj.resolve_sentences(sentences) and returns: result